1 code implementation • 14 Mar 2020 • Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee, Sarah Cherng, Giulia Landi, Matteo Danieletto, Joel T. Dudley, Cesare Furlanello, Riccardo Miotto
With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights.
1 code implementation • 31 Oct 2019 • Hao-Chih Lee, Matteo Danieletto, Riccardo Miotto, Sarah T. Cherng, Joel T. Dudley
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data.
1 code implementation • 1 Oct 2019 • Hao-Chih Lee, Sarah T. Cherng, Riccardo Miotto, Joel T. Dudley
Such requirements are particularly challenging for high-throughput imaging, where researchers must make decisions related to the trade-off between imaging quality and speed.
no code implementations • 15 Aug 2019 • Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli, Fabio Rinaldi, Venet Osmani
There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods.
no code implementations • 8 Nov 2018 • Marcus A. Badgeley, John R. Zech, Luke Oakden-Rayner, Benjamin S. Glicksberg, Manway Liu, William Gale, Michael V. McConnell, Beth Percha, Thomas M. Snyder, Joel T. Dudley
In this study, we trained deep learning models on 17, 587 radiographs to classify fracture, five patient traits, and 14 hospital process variables.